642 citations · 823 across the 9 of their papers we have counts for
4 papers · 1 filter
ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation
Salvatore Romano, Marco Grassia, Pietro Liò +1
Discrete diffusion models are a prominent family for graph generation, but standard class-conditional mechanisms embed the class signal in the denoiser during training, tying the c…
Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning
Salvatore Romano, Marco Grassia, Giuseppe Mangioni
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-wo…
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case
Marco Grassia, Manlio De Domenico, Giuseppe Mangioni
Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new w…
Learning Multi-Stage Sparsification for Maximum Clique Enumeration
Marco Grassia, Juho Lauri, Sourav Dutta +1
We propose a multi-stage learning approach for pruning the search space of maximum clique enumeration, a fundamental computationally difficult problem arising in various network an…